AI Solutions Architect

My name is Boopathi, and I m a Senior Technical Recruiter at Cloud Destinations LLC.

Please see the below job description and let me know if you are interested in this position.

AI Solutions Architect

6 Months

100% Remote

Position Overview: The AI Solution Architect at Switch is a senior technical leader responsible for designing and delivering end-to-end AI-powered solutions for enterprise clients. This role bridges deep AI/ML expertise with practical systems architecture - translating complex business challenges into scalable, secure, and cost-effective intelligent systems.

Working at the intersection of cloud infrastructure, data engineering, and applied AI, the AI Solution Architect drives architecture decisions across the full solution lifecycle - from initial discovery and design through delivery, operationalization, and continuous improvement. This individual serves as a trusted technical advisor to clients and a cross-team leader within Switch.

Key Responsibilities Architecture Design & AI Patterns

• Lead end-to-end solution architecture for AI/ML workloads including Retrieval-Augmented Generation (RAG), autonomous and multi-step agent systems, and large-scale inference pipelines.
• Define reference architectures and repeatable design patterns for generative AI, conversational AI, and predictive analytics solutions.
• Evaluate and select appropriate AI frameworks, foundation models (proprietary and open-source), vector databases, and orchestration layers based on use-case requirements.
• Design context management, prompt engineering strategy, and model fine-tuning or adaptation approaches where applicable.

Security, Privacy & Governance by Design

• Embed security and privacy controls at every layer of the AI architecture - data ingestion, model serving, API exposure, and user interfaces.
• Architect role-based and attribute-based access controls, data residency requirements, PII/PHI handling, and compliance-aligned logging.
• Define AI governance frameworks covering model risk, bias monitoring, explainability, and responsible AI usage policies in alignment with client standards and regulatory requirements.
• Conduct threat modeling and security reviews for AI-integrated systems, including LLM-specific attack surface considerations (prompt injection, data exfiltration, jailbreaking). Scalability, Resiliency & Performance Engineering
• Architect solutions that meet enterprise-grade requirements for scale, fault tolerance, and availability - from prototype to production at petabyte-scale data volumes.
• Analyze and optimize latency/throughput tradeoffs across inference serving, data retrieval, and downstream API chains.
• Design cost-optimized architectures balancing compute (GPU/TPU/CPU), storage tiering, and managed service costs - with FinOps practices embedded from day one.
• Develop and validate resiliency patterns including multi-region failover, circuit breakers, graceful degradation, and chaos engineering readiness. Platform & Tool Selection
• Evaluate and recommend AI/ML platforms, cloud providers (AWS, Azure, Google Cloud Platform), and managed AI services (Azure OpenAI, Amazon Bedrock, Vertex AI, etc.) based on technical fit and total cost of ownership.
• Guide toolchain selection across the MLOps lifecycle: data pipelines, experiment tracking, model registries, serving infrastructure, and monitoring.
• Assess build-vs-buy tradeoffs and open-source ecosystem options, translating recommendations into actionable procurement and roadmap guidance for clients. Integration Architecture
• Design integration patterns connecting AI components with enterprise systems - ERP, CRM, ITSM, data warehouses, and operational databases.
• Architect API gateways, event-driven pipelines, and streaming data integrations that feed AI systems with real-time and batch context.

Define identity and access management (IAM) patterns for AI service authentication, including federated identity, OAuth 2.0/OIDC flows, and secrets management (vaults, rotation, zero-trust network access).